Within the realm of automated data analysis and feature recognition systems, such as disclosed by Brinson, et al., in U.S. patent application 2007/0244844, which is incorporated by reference in its entirety herein, or any user-specified, preset, or automatically determined application or engine intended for use in the same or a similar manner, data discrimination is a “make or break” scenario insomuch as its accuracy and reliability are as much a product of the quality and legitimacy of the data being processed as they are the user's ability to effectively differentiate minute variances in the data and train the data correctly. Not only might information be lost in the translation of digital data from its raw form into a convenient, human perceivable output format, but once the data is converted, the burden then falls upon the user to make oft times indiscernible distinctions and selections in the ambiguous data. This can result in errors in training that have the potential to cause data and feature misidentification, such as inter alia false-positive or false-negative results, algorithm confusion, and/or data confusion once these errantly identified features are used as a foundation to process real-world data sets.
The underlying problem within most data analysis and feature recognition systems is the need to discriminate, conglomerate, and/or associate features, which can exist in a potentially multivalent, large pool of data, in accordance with relative human perception, interpretation, and visualization of the data. A feature, as recognized by the system, is simply an association of specific, finite data values and patterns that are characteristic of an entity deemed existent by a user. While specific data characteristics can be trained into the system as representative of the feature, the feature itself is merely a concrete representation of an abstract human interpretation, which is certainly fallible due to the occurrence of human biases, the ability to accurately achieve and interpret alternate renderings, etc.
While the data analysis and feature recognition industry has made strides in mitigating the occurrences of feature misidentification through the use of specialized and/or alternative visualization and feature recognition options, which can be used to extenuate the incidences of false-positives, the fact remains that most systems lack the fundamental capability to relate human perception and discernment of features in a way most amicable to proper sagaciousness of features present within a given data set or selection therein. Many current systems typically fail to reconcile bad or erroneous data; to provide for redundancy or the evaluation of compound or more complex features (e.g., the evaluation of this feature AND that feature together, this feature OR that feature together, this feature AND NOT that feature together); to specify data sensitivity (so as to delineate or mitigate erroneous or deviated results); to conglomerate features into more complex features (e.g., positive identification of the feature “cancer” requires certain criteria to be fulfilled); and/or to allow data modality and submodality independence and cooperation (for evaluation of data of different types, sources, modalities, submodalities, etc.). As such, the data discrimination capabilities currently prevalent in modern data analysis and feature recognition systems do not afford users the leniency needed when attempting to make discriminations in potentially enigmatic data. Subsequently, data training and processing using these faultily identified features is inaccurate at best or entirely useless.